CS 15-892
Foundations of Electronic Marketplaces
Tuomas Sandholm & John Dickerson
Computer Science Department
Carnegie Mellon University
Course web page:
www.cs.cmu.edu/~sandholm/cs15-892F15/cs15-892.htm
EXAMPLE APPLICATIONS
EXAMPLE MARKETS WITH
MONEY
Combinatorial sourcing (live auctions & RFPs/RFQs)
[Screen shots from Sandholm “Very-Large-Scale Generalized Combinatorial Multi-Attribute Auctions: Lessons from Conducting $60 Billion of Sourcing”]
Bidder side
Bid-taker (buyer) side
Spectrum auctions
[Screen shots from 2015 AWS-3 auction: $45 billion, 341 rounds]
Incentive auction
Need enough, but not all spectrum is equivalent
Incentive Auction
Spectrum
Existing TV
broadcasters
Payment
Reverse Auction
and Repacking
Spectrum
Payment
Forward
Auction
Spectrum
Future Wi-Fi
broadcasters
Payment
Minimize
Current channel allocation statistics
Stations currently allocated to channel 13
Sponsored search
Display advertising
EXAMPLE MARKETS WITH
“FUNNY” MONEY
Combinatorial course allocation
[Screen shots from Budish et al. working paper 2015]
Bidding
Dynamic exclusions
EXAMPLE MARKETS
WITHOUT MONEY
Kidney exchange
Patient 1
Patient 2
Donor 1
Donor 2
Pair 1
Pair 2
Example applications…
• B2B (business-to-business)
– Sourcing
• Also buying consortia (e.g., healthcare GPOs,
Covisint, Trade-ranger)
• IntercontinentalExchange, Inc. (acquired
ChemConnect 6/2007)
• B2C (business-to-consumer), e.g. goods, debt
• C2C (consumer-to-consumer), e.g. eBay
• Task and resource allocation in computer systems
(networks, computational grids, storage systems…)
• Electricity markets
• Transportation exchanges
• Stock markets
• Collaborative filtering
Motivation
Perspectives
• Leverage increasing computing (and communication) power to
increase economic efficiency
– E.g. running more complex mechanisms
• Combinatorial auctions [S. ICE-98, IJCAI-99, AIJ-02, AIJ-03, AAAI-04, Mgmt Sci, …]
• Expressive competition [S. Interfaces-06, IAAI-06, IJCAI-07, AI Mag-07, Market
Design Handbook 13, …]
• Expressive charity donations [Conitzer & S. EC-04, AIJ-11]
• Voting ...
• Capitalize on piles of info - unlike traditional market mechanisms
– E.g. automated mechanism design (AMD) [Conitzer & S. UAI-02,…]
• Revenue-maximizing combinatorial auctions? [Likhodedov & S. AAAI-04, AAAI-05, Operations Research
2016; Tang & S. IJCAI-11, AAMAS-12]
• Sponsored search, display ads, TV ads, …
Automated negotiation systems
• Agents search & make contracts
– Through peer-to-peer negotiation or a mediated marketplace
– Agents can be real-world parties or software agents that work on
behalf of real-world parties
• Increasingly important from a practical perspective
Fertile, timely, important research area
• Deep theories from game-theory & CS merge
– Started together in the 1940’s [Morgenstern & von Neumann]
– There were a few decades of little interplay
– Upswing of interplay in the last few years
• In this setting the prescriptive power of game theory really comes into play
– Market rules need to be explicitly specified
– Software agents designed to act optimally, unlike humans
– Computational capabilities can be quantitatively characterized, and prescriptions can
be made about how the agents should use their computation optimally
• Optimization has recently become scalable enough to make these things practical
– Custom integer programs for clearing problems
– Custom (e.g., convex) optimization for computing strategies
• The applications change the world
This course
• Covers
– The most relevant classic results from game theory
– The state-of-the-art through recent research papers
• Many of them have not even been published yet
• Covers
– game-theoretic aspects
– computational aspects
– and most importantly, the intersection
Systems with self-interested agents
(computational or human)
•
Mechanism (e.g., rules of an auction) specifies legal actions for each agent & how
the outcome is determined as a function of the agents’ strategies
•
Strategy (e.g., bidding strategy) = Agent’s mapping from known history to action
•
Rational self-interested agent chooses its strategy to maximize its own expected
utility given the mechanism
=> strategic analysis required for robustness => noncooperative game theory
•
But … computational complexity
– In executing the mechanism
• E.g. combinatorial auctions NP-complete & inapproximable to clear
– In executing a strategy
• How should computationally bounded agents play strategically?
–
–
Costly or limited computing [S. ICMAS-96, IJEC-00; Larson&S. AIJ-01, AAMAS-02, TARK-01, AGENTS-01 WS, EC-04, AAMAS-05…]
Optimal mechanism design for such agents?
• (Voting) mechanisms that are hard to manipulate [Conitzer&S. AAAI-02, IJCAI-03, TARK-03, JACM-07, …]
–
–
No voting mechanism is usually hard to manipulate [Conitzer&S. AAAI-06, …]
What about multistage mechanisms and/or adding a bit of randomization in the mechanism?
• Mechanisms that do better if the agents cannot solve their computational problems [Conitzer & S. LOFT-04; Othman & S.
GAMES-08, COMSOC-08, SAGT-09]
How these techniques
can/could play a role in
different stages of an
ecommerce transaction
Automated negotiation techniques
in different ecommerce stages
•
1. Interest generation (vendors compete for customers’ attention)
– Sponsored search
• Search keyword auctions (Google, Baidu, Yahoo!, Bing)
– Bid optimization vendors
• Display ad markets (Yahoo!, DoubleClick (now part of Google), Right Media
(now part of Yahoo!), adECN (now part of Microsoft), Baidu, …)
– Funded adlets that coordinate
• Avatars for choosing which ads to read
• Customer models for choosing who to send ads and how much $ to offer
•
2. Finding
– Simple early systems: BargainFinder, Jango
– Meta-data, XML
– Standardized feature lists on goods to allow comparison
• How do these get (re)negotiated
– Different vendors prefer different feature lists
– Shopper agents need to understand the new lists
– How do algorithms cope with new features?
– Want to get a bundle => need to find many vendors
Automated negotiation techniques in
different ecommerce stages...
•
3. Negotiating
– Advantages of dynamic pricing
• Right things sold to (and bought from) right parties at right time
• World becomes a better place (social welfare increases)
dynamic
– Further advantages from discriminatory pricing
• Can increase social welfare (e.g., if production increases)
– Fixed-menu take-it-or-leave-it offers -> negotiation
• Cost of generating & disseminating catalogs?
• Other customers see the price?
• Negotiation overhead?
• Personalized menus
Pricing
static
nondiscriminatory
discriminatory
– Could check customer’s web page, links to & from it, what other similar
customers did, customer profiles
• Generating/printing the menu may be intractable, e.g. mortgages 530
– Negotiation can focus the generation, but vendor may bias prices &
offerings based on path
– Preferences over bundles
– Coalition formation
Automated negotiation techniques in
different ecommerce stages...
• 4. Contract execution
– Digital payment schemes
– Safe exchange
• Third party escrow companies (1- or 2-sided)
• Sometimes an exchange can be carried out
without enforcement by dividing it into chunks
[Sandholm&Lesser IJCAI-95, Sandholm96,97, Sandholm&Ferrandon ICMAS-00, Sandholm&Wang AAAI-02]
• 5. After sales
Agenthood,
utility function,
rationality & bounded rationality,
evaluation criteria of multiagent systems
Agenthood
• We use economic definition of agent as locus of self-interest
– Could be implemented e.g. as several mobile “agents” …
• Agent attempts to maximize its expected utility
• Utility function ui of agent i is a mapping from outcomes to reals
– Can be over a multi-dimensional outcome space
– Incorporates agent’s risk attitude (allows quantitative tradeoffs)
• E.g. outcomes over money
Lottery 1: $0.5M w.p. 1
ui
Risk averse
1
Lottery 2: $1M
$0
w.p. 0.5
w.p. 0.5
Agent’s strategy is the
choice of lottery
Risk neutral
0.5
Risk seeking
0
0
0.5
1
Risk aversion => insurance companies
M$
Utility functions are scale-invariant
• Agent i chooses a strategy that maximizes expected utility
• maxstrategy Soutcome p(outcome | strategy) ui(outcome)
• If ui’() = a ui() + b for a > 0 then the agent will choose the
same strategy under utility function ui’ as it would under ui
– (ui has to be finite for each possible outcome; otherwise expected utility could
be infinite for several strategies, so the strategies could not be compared.)
Full vs bounded rationality
Full
rationality
Environment
Bounded
rationality
Environment
Perceptions
Actions
Agent
Perceptions
Actions
Agent
Reasoning
machinery
solution quality
Descriptive vs. prescriptive
theories of bounded rationality
worth of solution
time
deliberation cost
Criteria for evaluating multiagent systems
•
•
•
•
•
•
•
•
•
•
Computational efficiency
Distribution of computation
Communication efficiency
Social welfare: maxoutcome ∑i ui(outcome)
– Requires cardinal utility comparison
– … but we just said that utility functions are arbitrary in terms of scale!
Surplus: social welfare of outcome – social welfare of status quo
– Constant sum games have 0 surplus. Markets are not constant sum
Pareto efficiency: An outcome o is Pareto efficient if there exists no other
outcome o’ s.t. some agent has higher utility in o’ than in o and no agent
has lower
– Social welfare maximization => Pareto efficiency
Individual rationality: Participating in the negotiation (or individual deal) is
no worse than not participating
Stability: No agents can increase their utility by changing their strategies
Symmetry: No agent should be inherently preferred, e.g. dictator
…